An Information-Theoretic Efficiency of the "Slow Calcium Wave" in the Brain from a Computing Architecture Perspective

Abstract

Modern Artificial Intelligence (AI) and Deep Neural Network (ANN) architectures are rapidly approaching a fundamentalthermodynamic bottleneck characterized by exorbitant energy usage and physical thermal throttling. Conversely, the humanbrain achieves ultra-efficient distributed computation at an average power consumption of merely 20 W. Current paradigms incomputational neuroscience and neuromorphic hardware engineering remain heavily anchored to a neuron-centric framework,prioritizing millisecond-scale electrical spiking while dismissing non-neuronal glial mechanisms as passive support structures.This paper presents a novel computing paradigm that re-conceptualizes brain architecture into a Dual-Layer System: anOperational Layer composed of rapid neuronal circuits, and an Infrastructural Layer driven by astrocytic networks. Weformalize the "slow calcium wave" (100to 102 second timescale) as an active information-theoretic governor implementing dynamic congestion control and energy scheduling.We demonstrate that the deliberate time lag between rapid synaptic firing and slow astrocytic calcium signaling is not anevolutionary defect, but a critical design mechanism for dynamic throttling, preventing excitotoxic kernel panic whileguaranteeing long-term system uptime (100% availability). Finally, we offer engineering guidelines for embedding an ArtificialGlia Layer into next-generation neuromorphic chips and Large Language Model (LLM) serving hardware.